Validation of the component degradation simulation tool (CODES)

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Validation of the component degradation simulation tool (CODES) A. Keating, Member, IEEE , P. Gonçalves, A.Zadeh, Member, IEEE, M.Pimenta, S. Coutinho, P.Brogueira, E.Daly

Abstract—SEE mechanisms in modern technologies radiation have increasing complexity. This paper reviews CODES and SVFIT (Sensitive Volume Fit) models, which have been developed to evaluate device sensitivity to radiation and predict SEU rates. These models are the object of an engineering tool that is currently under development. The models are based on the Geant4 radiation transport program combined with irradiation test data. For models validation the Reference SEU Monitor (RSEUM) has been fully simulated. The models allow a very good reconstruction of SEU cross-section curves for ions and protons based on ion test data simulation. The description of direct and indirect ionization profiles at the RSEUM sensitive volume for all particles and energies is presented. This paper describes the models, the overall framework and some of the most relevant results. Index Terms— Radiation, Single event effects, Reference SEU Monitor, Geant4, CODES, SVFIT

I. INTRODUCTION

T

HE hostile space radiation environment affects all spacecraft, system, sub-system down to the component level. Successful prediction of radiation induced degradation thus requires in-depth knowledge of the space radiation environment and accurate device sensitivity determination. Monte Carlo (MC) simulation tools (such as Geant4 [1]) are increasingly employed for the study of radiation effects and analysis application, both for space environment aspects and Manuscript received September 4, 2011. This work was supported in part by the European Space Agency General Support Technology Program (ESA Contract 22381/09/NL/PA) and by the Portuguese Foundation for Science and Technology. A. Keating (corresponding author) is with Laboratório de Instrumentação e Física Experimental de Partículas, 1000-149 Lisbon, Portugal and with the European Space Agency, Postbus 299 - NL (phone: +31-71-565-3931; fax: +31-71-565-6637; e-mail: [email protected]). P. Gonçalves is with Instituto Superior Técnico, Lisbon, Portugal and with Laboratório de Instrumentação e Física Experimental de Partículas, 1000-149 Lisbon, Portugal (email: [email protected]). S. Coutinho is with Cyberoffice, Travessa da Figueiroa, 44, 4050-257 Porto, Portugal ( email: [email protected] ). M. Pimenta is with Instituto Superior Técnico, Lisbon, Portugal and with Laboratório de Instrumentação e Física Experimental de Partículas, 1000-149 Lisbon, Portugal (email: [email protected]). A. Zadeh is with the European Space Agency, Postbus 299 - NL (e-mail: [email protected]). P. Brogueira is with Instituto Superior Técnico, Lisbon, Portugal (email: [email protected]). Eamonn Daly, is with the European Space Agency, Postbus 299 - NL (email: [email protected])

EEE component related issues. In particular the European Space Agency (ESA) has been active in extending the capabilities of Geant4 as a MC particle transport simulation tool enabling its use for space related applications. CODES, the integrated radiation environment and component degradation simulation tool, has been developed under ESA contracts in order to integrate radiation environment characterization and detailed simulation of device sensitivity. SVFIT is the model developed in CODES framework that using Geant4 transport program and interfaced with device irradiation test data allows the determination of the device sensitivity. For models validation the Reference SEU Monitor [2] has been fully simulated, drift and diffusion sensitivities were characterized and test data was reconstructed. II. CODES FRAMEWORK AND INTERFACES CODES, the software framework provides the tools for analyzing and predicting radiation effects electronic components. The detailed Geant4 based application enables the computation of total ionizing dose (TID) degradation and Single event effects (SEE). However in the present version to be available under the web based interface, CODES is optimized for single event upsets (SEU). It consists of two main modules, one responsible for the detailed simulation of device response function and sensitivity determination, SVFIT; and the other responsible for the transport of the environment input radiation through the characterized device and prediction of degradation under inflight conditions. A web based user-friendly interface was developed together with a pre-processor in order to control the communication flow between the user and the code and the different modules of the framework. Additionally interface and pre-processor manage input information and generate output data. A. The Sensitive Volume Fit Model (SVFIT) The SVFIT models the device sensitivity to radiation by deriving the energy deposition distribution in the active region and fitting the device experimental response function. The threshold is defined by the lowest LET ion with an experimental cross-section. SVFIT offers the user the possibility of simulating different sensitive volume geometries such as: rectangular parallelepiped (RPP), trapezoid (TRP) or a polycone (PLC). SEU threshold, shape and size that best fit

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5$'(&63URFHHGLQJVPE3 the sensitive volume (SV) are then computed by the preprocessor. The minimum required inputs for SVFIT are the ion cocktail description, including information on the specific ion, atomic number, mass, charge, energy and experimental cross-sections. If no other input is given SVFIT assumes a block of silicon and use RPP and TRP as SV shapes to reproduce experimental data. In case the user has additional information on the device, SVFIT is capable of handling inputs as efficiency matrix, user defined dimensions and materials, packaging layers and shielding information.

trapezoid (triangle) shapes, as described in Tab.1, with experimental data (diamond). Results show that the trapezoidal SV reproduces the device response function with accuracy better than 10%. The rectangular parallelepiped yields a step function which is not a good description of the experimental data distribution.

III. SIMULATION OF THE REFERENCE SEU MONITOR A. Simulation setup The device selected to validate CODES models and framework was the 4Mbit ATMEL AT60142F Static Random Access Memory (SRAM) comprehensively characterized as part of ESA’s “Reference SEU Monitor” (RSEUM) development activity [2]. Fig.1. and 2 illustrate the most complex device geometry simulated with SVFIT for the RSEUM. In this setup the device active regions are located under 6 transistors of each bit cell.

Fig. 1. Simulation setup: 4 bit cells with 6 sensitive volumes each.

Fig.2. depicts the vertical profile of the device were the packaging layer (in light grey) is placed on top of the component and the active regions (in black) has a thickness and shape to be determined by SVFIT. The metallization, vias and oxide layers were simulated as an average top layer consisting of tungsten, silicon dioxide, aluminum, copper and titanium according to vendor and processes information.

Fig. 3. Reconstruction of RSEUM response function

Error bars are given by the uncertainties of the SVFIT cross-section calculation method. They over estimate the real calculation errors but give a measure of the SVFIT accuracy at different LET values. C. Energy loss distribution at the sensitive volume Fig.4 shows the energy loss across the SV for RPP and TRP shapes. It can be seen that while the RPP illustrates a clear peak due to the primary particles energy loss entirely contained in the SV, the TRP sensitive volume exhibits a clear modulation of the energy distribution with the ion path length distribution across the SV. This modulation is responsible for the excellent performance of the TRP volume to reconstruct SEU cross-section curve in the knee region up to saturation. Fig.4. a) also shows a tail in the low energy range. Events that deposit these lower energies are events that cross the device near a sensitive volume and partially deposit energy in neighbor SVs. D. Considerations on ATMEL AT60142F SRAM data One of the motivations to analyze and simulate irradiation test data of ATMEL AT60142F SRAM device was the fact that SEU cross-section curve does not exhibit saturation for the tested LET range. This effect is smoothed by the logarithmic scale. The ATMEL AT60142F SRAM was not tested against Multiple Bit Upsets (MBU) [2]. Therefore this effect is believed to be due to MBUs.

Fig. 2. Vertical profile of simulated geometry.

B. Ion independent sensitive volume In a first iteration the SV was assumed to be constant and therefore ion-independent. Fig.3. compares the RSEUM response function reconstructed with the rectangular parallelepiped (square) and

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Fig. 5. Effective radius increment above 18MeV.cm2/mg.

a)

b) Fig. 4. Energy Loss in the sensitive volume: a) RPP and b) TRP

E. Drift/Diffusion ATMEL AT60142F SRAM SEU response function can be defined as a combination of two effects: 1st a standard SEU cross-section curve that saturates around 10MeV.cm2/mg, plus an increasing LET-dependent perturbation. According to literature [3], the solution for the standard SEU cross-section curve is to calculate the shape of the sensitive volume and then the distribution of ion path lengths through the sensitive region. Reconstructing the SV geometry employing SVFIT is then expected to reproduce the first effect. MBUs are usually described as being caused by diffusion in the substrate rather than drift. Diffusion mechanisms are also known to cause a sensitive area LET-dependent enhancement [4]. Studies preformed on 0.18μm CMOS technologies [4] show thresholds for diffusion LET-dependent effect of the order of 20 to 30MeV.cm2/mg. However those thresholds are known to be highly technology dependent. In order to quantify this effect for the 0.25μm ATMEL AT60142F SRAM, a model was developed based on the analysis of the ESA test data for the RSEUM. All generated test data was analyzed in terms of effective LET dependent transversal area increment above 18MeV.cm2/mg. Fig.5 depicts all experimental test data obtained for the RSEUM in different facilities (RADEF, UCL, GANIL, LNS), in units of effective increased radius, reff, assuming a transversal circular symmetry. reff =

1

π

[σ (LET ) − σ (LET = 18MeVcm sat

2

)]

/ mg ⋅10 4 [ μm / bit ]

Eq. 1. Effective radius increment above 18MeV.cm2/mg.

F. Ion-dependent sensitive transversal area increment A new geometry, a polycone (PLC), was simulated with SVFIT according to the model of Fig.5, as described in Tab.1. Fig.6. compares the RSEUM response function reconstruction for polycone geometry (circle) including the diffusion LET-dependent model, the ion-independent trapezoid (triangle) SV and experimental data (diamond). From Fig.6 it can be seen that both the ion-independent SV (TRP) and the LET-dependent SV (PLC) have a very good agreement with experimental in the knee region. However for ions with LET above 18MeV.cm2/mg, the diffusion model reproduces fairly well the device response function while the fixed SV under estimates the experimental SEU crosssections.

Fig.6. Reconstruction of RSEUM response function.

G. Best SV shape, dimension and SEU threshold SVFIT computes the SEU threshold as the minimum energy loss in the SV to generate an upset. The pre-processor is responsible for the estimation of the best reconstructed SV. The algorithm computes the relative dispersion of each reconstructed cross-section to the experimental value and averages them over all simulated ions for each geometry configuration. The SV that better reproduces of experimental data as well as the corresponding SEU threshold in units of deposited energy are then returned to the user. Tab.1. lists SVFIT output for each of the geometries discussed previously.

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Tab.1. SVFIT output.

IV. DIRECT AND INDIRECT IONIZATION MECHANISMS Another important result obtained with CODES is the capability of reconstructing proton SEU cross-section based on ion test data. This has been verified with RSEUM but needs to be further validated with other devices. CODES employs the Geant4 physics including hadronic, electromagnetic and low energy neutron physics [5] [6]. The tool is therefore capable of simulating fragmentation and computes indirect ionization cross-sections for all particles. This effect is visible in Fig. 4 a) and b) for ions and in Fig. 7 for protons. The distribution of energy deposition in the sensitive volume for protons and low LET ions exhibit peaks with low statistics above the critical energy. These peaks are generated by recoils hitting the sensitive region of the device and have much lower cross-section than direct ionization peaks. A. Proton response function The RSEUM has been tested with protons at PSI [2]. Aiming at determining proton SEU cross-section with SVFIT, proton beams have been simulated with the same energies as the ones employed in irradiation tests at PSI. The device sensitivity was defined as determined by SVFIT based on ion test data.

Fig.8. Reconstruction of RSEUM response function to proton irradiation.

It can be seen that CODES shows a very good performance in reproducing proton SEU cross-section for the RSEUM based on ion test data analysis. The analysis of energy deposition at the sensitive volume shows that for the RSEUM the protons SEUs are solely due to indirect ionization by fragmentation and recoils production. The error bars and deviations from expected values are mostly due to low statistics and strongly depend on the SEU threshold determination accuracy. V. DISCUSSION In complex devices such as integrated circuits or memory devices, the sensitivity is a combination of different active nodes in the device. Therefore the SV as reconstructed by SVFIT can not be seen as a real microscopic sensitive volume; rather it is a representation of the average sensitivity of the device. However predicted SV geometry will help identify the dominant mechanisms involved in SEU generation. As illustrated by the results previously discussed in the paper the drift mechanisms dominate, however at high LET values the diffusion has an increasing contribution. Following a number of experiments with various geometries the authors believe that the triangular geometries are a better representation of the SV possibly due to the fact that they better mimic the funneling shape. This is also in agreement with expected relative difference between funneling and depletion length [7] for a device with the RSEUM characteristics. VI. CONCLUSIONS AND FURTHER WORK

Fig.7. Energy Loss in the TRP sensitive volume by protons.

Fig. 7 depicts the distribution of energy loss in the SV by protons of different energies. The first broad peak under the threshold is due to direct ionization. Above 0.1MeV, energy is lost in the SV by recoils, this value is lower that the SEU threshold for all the geometries, as described in Tab.1. Fig.8 compares the proton SEU cross-section obtained for the different geometries previously described: RPP (square), TRP (triangle) and PLC (circle) with experimental data obtained at PSI (diamond).

This paper describes CODES framework and the SVFIT model. The framework, as a modular tool, including the interface and the pre-processor, is design with flexibility in mind for future model updates. The first version is expected to be fully operational and validated as an engineering tool by the end of 2011. Results show that the accurate determination of the SV shape and dimensions enables the reconstruction of experimental cross-section curve, for heavy ions and protons at different energies and charge state. Employing SVFIT without implementing the diffusion

5$'(&63URFHHGLQJVPE3 model a very good agreement with experimental data is obtained in the knee region up to saturation (within 5 to 10%). However when the diffusion model is also implemented the reconstruction of the device response function improves considerably for all LET values. Results show an excellent reconstruction of proton SEU cross-sections based on ion test data analysis for the RSEUM. SVFIT, relying on ground based test data and enables a qualitative interpretation of the physics mechanisms contributing to SEU generation. CODES is expected to enable SEU rate predictions with a very high accuracy (within 5%). This will be further validated with other memory devices and compared with other simulation tools.

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